Integrating geospatial and multi‐depth laboratory spectral data for mapping soil classes in a geologically complex area in southeastern <scp>B</scp>razil

Integrating geospatial and multi‐depth laboratory spectral data for mapping soil classes in a geologically complex area in southeastern Brazil

European Journal of Soil Science (2015)

Gustavo M. Vasques José Alexandre Melo Demattê Raphael A. Viscarra Rossel Leonardo Ramírez-López Fabrício da Silva Terra Rodnei Rizzo Carlos Roberto de Souza Filho

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Abstract:

Summary Soil mapping across large areas can be enhanced by integrating different methods and data sources. This study merges laboratory, field and remote sensing data to create digital maps of soil suborders based on the B razilian S oil C lassification S ystem, with and without additional textural classification, in an area of 13 000 ha in the state of S ão P aulo, southeastern B razil. Data from 289 visited soil profiles were used in multinomial logistic regression to predict soil suborders from geospatial data (geology, topography, emissivity and vegetation index) and visible–near infrared (400–2500 nm) reflectance of soil samples collected at three depths (0–20, 40–60 and 80–100 cm). The derived maps were validated with 47 external observations, and compared with two conventional soil maps at scales of 1:100 000 and 1:20 000. Soil suborders with and without textural classification were predicted correctly for 44 and 52% of the soil profiles, respectively. The derived suborder maps agreed with the 1:100 000 and 1:20 000 conventional maps in 20 and 23% (with textural classification) and 41 and 46% (without textural classification) of the area, respectively. Soils that were well defined along relief gradients ( L atosols and A rgisols) were predicted with up to 91% agreement, whereas soils in complex areas ( C ambisols and N eosols) were poorly predicted. Adding textural classification to suborders considerably degraded classification accuracy; thus modelling at the suborder level alone is recommended. Stream density and laboratory soil reflectance improved all classification models, showing their potential to aid digital soil mapping in complex tropical environments.

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Digital Soil Mapping

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Soil Geostatistics and Mapping

Geochemistry and Geologic Mapping

Soil and Land Suitability Analysis

10.1111/ejss.12255

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Identification of Soil Types and Boundaries Based on Soil Taxonomy——A Case Study of Dading Mountain of Jurong

Soils (2011)

WU Ming-wei

Soil survey technology based on soil taxonomy is still at the research stage in China.Taking example by soil survey method based on traditional genesic classification,this paper introduced the identification of soil types and soil boundaries based on soil taxonomy.Soil profiles were located by the traditional method and the number of profile was decided by terrain classification and mapping precision,soil types were determined by using diagnostic horizons and diagnostic characteristics.Soil boundaries were identified on the bases of demarcation points in different survey lines determined by the interpolation method and connected based on remote sensing images.The study showed that soil types and soil boundaries closely contacted with parent material,topography,vegetation,land use pattern and other landscape factors,thus,it is necessary to give full consideration to the landscape factors in the soil survey.

USDA soil taxonomy

Soil survey

Digital Soil Mapping

Soil horizon

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Deriving World Reference Base Reference Soil Groups from the prospective Global Soil Map product — A case study on major soil types of Africa

Geoderma (2015)

Vince Láng Márta Fuchs Tamás Szegi Ádám Csorba Erika Michéli

Digital Soil Mapping

USDA soil taxonomy